The present invention relates to a real time status monitoring method and an apparatus therefor, and more particularly to a data processing method in a real time status monitoring system which determines a status and makes a decision on a real time basis based on a huge amount of data information which randomly varies with respect to an object, and a system therefor.
Specifically, the present invention is applicable to a trading support system based on market quotation in financial and security fields, a support system for measurement, monitor, control and decision making, comprising a number of sensors, and a support system for status determination and decision making for aviation control or traffic control.
While the present invention is applicable to various fields, it will be explained as a trading support system in the financial and security field.
An outline of the support system for making a decision in accordance with status such as the trading support system in the financial and security field is shown in FIG. 12, in which market quotation information which contains a huge number of varying stock and credit prices is received by a receiver 1, and the latest data to be monitored is supplied to a real time status sensing table 2 in a system. Two kinds of data bases are provided corresponding to kinds of informations. Namely, a time serial data base 4 which contains market quotation information and a data base 5 which contains information necessary for decision making support processing, for example, portfolio data of financial assets, are provided. Based on the market quotation status, a decision making support processor 3 evaluates the asset portfolio on a real time basis, determines buying and selling timing by a moving mean method, generates support information for reassembling the asset portfolio, and outputs them to a display unit 6. By combining the real time status sensing table 2 with the data stored in the data base 4 at a desired time in a desired form, information for most effective decision to a decision maker can be generated and provided in accordance with the status. In this case, there is a time delay between the reading of the market quotation information and the display of the information to the decision maker. It is important to reduce the time delay as much as possible.
As a real time processing method applicable to solve the above problem, a known multi-target tracking processing method may be used. In this method, multi-targets to be monitored are observed at a constant time interval and status inferences of all of the multi-targets are updated based on the observation. ("Improvement in Correlation Precision in Status Inference Using Multi-Sensors" by Kosaka et al., SYSTEM AND CONTROL, Vol. 27, No. 8 (1983), "A track correlation algorithm for multi-sensor integration" IEEE/AIAA 5th DIGITAL AVIONICS SYSTEMS CONFERENCE, Oct. 31-Nov. 3, 1983, PP. 10.3.1-10.3.8). In the known multi-target tracking processing, the status changes of all targets to be monitored are always due to the movement by a physical law. Thus, the status of the target at any time can be predicted by a Kalman filter within a certain range of error even if the observation and the status inference are done at the constant time interval. Accordingly, it is an effective processing method in an aviation control system and a command and role system.
The above method may be applied to the decision making support processing of the trading support system shown in FIG. 12. It is assumed that the market quotation information (stocks and credits whose data are varying and prices thereof) has been stored in the real time status sensing table. Decision making support information processing groups {Fi} are processed at a constant time interval as shown in FIG. 2. All information processing groups {Fi} are processed in accordance with the content of the real time status sensing table 2 at a time Ti, and the results are supplied to the display unit. The same processing is performed to the content of the real time status sensing table at a time T.sub.i+1 (=Ti+.DELTA.T) which is .DELTA.T time later than the time Ti. The time .DELTA.T must be larger than a sum of processing times for all information processing groups.